:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Author:	Aishima, Kensuke
Format:	Preprint
Published:	2026
Subjects:	Statistics Theory Numerical Analysis
Online Access:	https://arxiv.org/abs/2602.09762
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Strong consistency of an estimator by the truncated singular value decomposition for an errors-in-variables regression model with collinearity
by: Aishima, Kensuke
Published: (2023)

Polynomial approximation of noisy functions
by: Matsuda, Takeru, et al.
Published: (2024)

Measure transport with kernel mean embeddings
by: Wang, L., et al.
Published: (2024)

A Fourier-based inference method for learning interaction kernels in particle systems
by: Pavliotis, Grigorios A., et al.
Published: (2025)

Reproducing kernel methods for machine learning, PDEs, and statistics
by: LeFloch, Philippe G., et al.
Published: (2024)

Enabling stratified sampling in high dimensions via nonlinear dimensionality reduction
by: Geraci, Gianluca, et al.
Published: (2025)

On posterior consistency of data assimilation with Gaussian process priors: the 2D Navier-Stokes equations
by: Nickl, Richard, et al.
Published: (2023)

A kernel method for the learning of Wasserstein geometric flows
by: Hu, Jianyu, et al.
Published: (2025)

Precision and Cholesky Factor Estimation for Gaussian Processes
by: Chen, Jiaheng, et al.
Published: (2024)

Mean-Square Convergence of a New Parameterized Leapfrog Scheme for Hamiltonian Systems Driven by Gaussian Process Potentials
by: Bhattacharya, Sourabh
Published: (2026)

Error analysis for a statistical finite element method
by: Karvonen, Toni, et al.
Published: (2022)

Extreme learning machines for variance-based global sensitivity analysis
by: Darges, John, et al.
Published: (2022)

Sequential multiple importance sampling for high-dimensional Bayesian inference
by: Binbin, Li, et al.
Published: (2025)

Posterior Covariance Structures in Gaussian Processes
by: Cai, Difeng, et al.
Published: (2024)

Gaussian Process Regression under Computational and Epistemic Misspecification
by: Sanz-Alonso, Daniel, et al.
Published: (2023)

Non-Asymptotic Analysis of Ensemble Kalman Updates: Effective Dimension and Localization
by: Ghattas, Omar Al, et al.
Published: (2022)

Dimension-Free Convergence of Diffusion Models for Approximate Gaussian Mixtures
by: Li, Gen, et al.
Published: (2025)

Linear cost and exponentially convergent approximation of Gaussian Matérn processes on intervals
by: Bolin, David, et al.
Published: (2024)

Consistent estimation with the use of orthogonal projections for a linear regression model with errors in the variables
by: Aishima, Kensuke
Published: (2023)

Data-driven Learning of Interaction Laws in Multispecies Particle Systems with Gaussian Processes: Convergence Theory and Applications
by: Feng, Jinchao, et al.
Published: (2025)

Gaussian Processes and Reproducing Kernels: Connections and Equivalences
by: Kanagawa, Motonobu, et al.
Published: (2025)

Sampling pseudospectrum for data-driven matrices
by: Wormell, Caroline
Published: (2026)

Asymptotic analysis of rare events in high dimensions
by: Katsevich, Anya, et al.
Published: (2025)

Sticky coupling as a control variate for sensitivity analysis
by: Darshan, Shiva, et al.
Published: (2024)

On the contraction rate of the posterior distribution for nonlinear PDE parameter identification
by: Fan, Yuxin, et al.
Published: (2026)

Covariance-Informed Subspace: an Adaptive Gradient-Free Input Dimension Reduction Method for Bayesian Inference
by: Polette, Nadège, et al.
Published: (2026)

Accelerating Langevin Monte Carlo via Efficient Stochastic Runge--Kutta Methods beyond Log-Concavity
by: Yang, Bin, et al.
Published: (2026)

A Closed-Form Transition Density Expansion for Elliptic and Hypo-Elliptic SDEs
by: Iguchi, Yuga, et al.
Published: (2025)

Boosted optimal weighted least-squares
by: Haberstich, Cécile, et al.
Published: (2019)

One-dimensional Tensor Network Recovery
by: Chen, Ziang, et al.
Published: (2022)

A global Lipschitz stability perspective for understanding approximate approaches in Bayesian sequential learning
by: Wang, Liliang, et al.
Published: (2025)

Blind free deconvolution over one-parameter sparse families via eigenmatrix
by: Ying, Lexing
Published: (2025)

A polynomial chaos approach for uncertainty quantification of Monte Carlo transport codes
by: Geraci, Gianluca, et al.
Published: (2024)

Computable error bounds for quasi-Monte Carlo using points with non-negative local discrepancy
by: Gnewuch, Michael, et al.
Published: (2023)

Multi-fidelity No-U-Turn Sampling
by: Ravi, Kislaya, et al.
Published: (2023)

Coverage errors for Student's t confidence intervals comparable to those in Hall (1988)
by: Owen, Art B.
Published: (2025)

An Approximation Theory Framework for Measure-Transport Sampling Algorithms
by: Baptista, Ricardo, et al.
Published: (2023)

Extrapolation and generative algorithms for three applications in finance
by: LeFloch, Philippe G., et al.
Published: (2024)

Sparse free deconvolution under unknown noise level via eigenmatrix
by: Ying, Lexing
Published: (2025)

Tensor-train methods for sequential state and parameter learning in state-space models
by: Zhao, Yiran, et al.
Published: (2023)